Flight trajectory prediction is crucial in maintaining the safety and predicting accidents in the National Airspace System (NAS). The reported work used Bayesian updating to achieve flight trajectory prediction and real-time risk assessment in the NAS. The trajectory simulation is done using NATS, a novel flights simulation platform. The model can consider multiple sources of uncertainties such as weather, human performance etc. Through Bayesian updating, the uncertainty in the model can be reduced given observable quantities. In this article, the Bayesian framework in updating model parameter through observation is introduced. The NATS simulation for a real accident scenario at SFO airport will be presented. In the presented framework, the risk probability is updated continuously using the aircraft location tracking information. The accident can be predicted well before it happens. A criterion for assessing the risk probability is developed under the NATS platform. The risk probability is evaluated based on the separation between aircrafts. It can work as a computer-aided algorithm for Air Traffic Management (ATM) aiming to help the ATC operator in preventing potential accidents.